A fund manager invests both the fund's assets and own private wealth in separate but potentially correlated risky assets, aiming to maximize expected utility from private wealth in the long run. If relative risk aversion and investment opportunities are constant, we find that the fund's portfolio depends only on the fu…
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48 results for “Investment Managers”
Deep RL optimizes goal-based investing strategies.
problem Optimizing investment strategies for achieving financial goals.
method Novel deep reinforcement learning approach for goal-based investing.
result Superior performance compared to benchmarks.
The paper uses clustering and integer programming to optimize stock selection for investment funds.
problem Maximizing profits and minimizing risk in stock markets.
method Data-oriented analysis and clustering techniques with integer programming.
result Reconstructed NASDAQ 100 index fund example demonstrates effectiveness.
Managing Risk in DeFi Portfoliosq-fin.PM
This paper assesses risks in DeFi investments.
problem Risks in decentralized finance investments.
method Overview of DeFi components and risk quantification methodology.
result Proposes an allocation methodology to integrate and quantify risks.
Investment managers face harder choices in green stocks due to reduced performance variability.
problem Difficulty in deploying talent in green stocks due to reduced performance variability.
method Analysis of S&P 500 firms' greenhouse gas emission levels and peer performance ratios.
result Performance variability has decreased in green stocks, making it harder for managers to choose.
Turnover-adjusted IR is always lower than classic IR, suggesting managers can improve performance by limiting turnover.
problem The classic relationship between IR and its determinants does not account for turnover costs.
method Mathematical derivations and simulations considering volatility of information coefficient and portfolio turnover.
result Turnover-adjusted IR is lower and managers can improve performance by limiting turnover.
The paper analyzes portfolio management in the Heston model, proposing new strategies.
problem Investment performance influenced by asset diversity and cash inclusion.
method Monte Carlo simulations in the Heston model, MACD and RSI technical analysis.
result New portfolio management strategies based on MACD and RSI.
The investment economy is a main characteristic of prosperous society. The investment portfolio management is a main financial problem, which has to be solved by the investment, commercial and central banks with the application of modern portfolio theory in the investment economy. We use the learning analytics together…
TDA improves cryptocurrency portfolio management.
problem Traditional methods fail to manage cryptocurrencies effectively.
method Topological Data Analysis (TDA) for identifying investment opportunities.
result TDA-based portfolio management outperforms traditional methods.
This paper explores portfolio management strategies to maximize alpha and minimize beta.
problem Maximizing returns while minimizing risk in investment portfolios.
method Examines asset allocation, diversification, active management, and risk management strategies.
result Combining these strategies optimizes portfolio performance.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
problem Lack of consistent and specific financial advisor expertise in personalized investment advice.
method Grounds financial advisor personas in fund disclosures, holdings transitions, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and grounded manager interpretation than generic baselines.
AI investors signal higher debt in ESG firms, boosting portfolio management.
problem Determining the value of ESG investing amid AI investment trends.
method Cross-sectional regressions of ESG scores and debt ratios of S&P 500 firms.
result ESG scores signal higher debt in firms, supporting ESG investing.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
problem Lack of consistent advisor expertise and difficulty in encoding it in LLM systems.
method Grounds financial advisor personas in fund disclosures, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and manager interpretation than generic baselines.
Upper bounds on utility for managing heterogeneous collectivised funds.
problem Managing pension funds with diverse investor preferences and mortality.
method Axiomatic approach to define optimal management strategies.
result Asymptotically optimal strategies for maximizing investor utility.
The study assesses carbon risk in investment portfolios and proposes new management strategies.
problem The impact of carbon risk on stock pricing and portfolio construction.
method Developed a BMG risk factor and estimated time-varying carbon beta using a multi-factor model.
result Carbon risk can be incorporated into portfolio construction to reduce unrewarded financial risks.
GenAI offers financial benefits but requires risk management.
problem Managing risks in financial applications of AI.
method Balancing AI's potential with risk control strategies.
result Proper risk management is essential for AI growth in finance.
A simple formula approximates AUM fees' cumulative costs.
problem Estimating the total cost of AUM fees over time.
method Intuitive explanation and analytical derivation of a formula.
result Investments lose almost Nε% of their value over N years with an annual fee of ε%.
We analyze a family of portfolio management problems under relative performance criteria, for fund managers having CARA or CRRA utilities and trading in a common investment horizon in log-normal markets. We construct explicit constant equilibrium strategies for both the finite population games and the corresponding mea…
Generative model learns investment strategies without explicit utility specification.
problem Challenges in modeling complex, multi-objective fund optimization.
method Generative adversarial network (GAN) framework that learns latent strategy representations.
result Framework captures diverse investment styles and realizations of optimization parameters.
An online framework improves investment management by making incremental updates.
problem Offline investment processes restrict silos from collectively pursuing a unified goal.
method Developed an online algorithm workflow for portfolio management.
result The online framework outperforms market benchmarks and reduces overfitting.
The paper fits cash management models to data using stochastic and linear programming.
problem Cash flow probability distribution assumptions in cash management models are relaxed.
method Stochastic and linear programming to fit models to data.
result A small random sample of data is sufficient to fit bound-based models.
Combines human and AI to optimize fund managers' investment decisions.
problem Improving fund managers' investment practices.
method Combines Inverse Reinforcement Learning and Reinforcement Learning.
result Improves fund managers' investment performance.
SimStock learns stock similarities for better investment management.
problem Challenges in identifying similar stocks due to non-stationary financial markets.
method Temporal self-supervised learning framework combining SSL and temporal domain generalization.
result SimStock outperforms existing methods in finding similar stocks.
We quantify the benefit of collectivised investment funds, in which the assets of members who die are shared among the survivors. For our model, with realistic parameter choices, an annuity or individual fund requires approximately 20\% more initial capital to provide as good an outcome as a collectivised investment fu…
In this paper we investigate novel applications of a new class of equations which we call time-delayed backward stochastic differential equations. Time-delayed BSDEs may arise in finance when we want to find an investment strategy and an investment portfolio which should replicate a liability or meet a target depending…
What Makes An Asset Useful?q-fin.ST
Given a new candidate asset represented as a time series of returns, how should a quantitative investment manager be thinking about assessing its usefulness? This is a key qualitative question inherent to the investment process which we aim to make precise. We argue that the usefulness of an asset can only be determine…
Paper introduces a new method for risk-sensitive investment management using RL.
problem Risk-sensitive portfolio management with unknown model parameters.
method Combines RL and risk-sensitive stochastic control with Gaussian perturbations for exploration.
result Endogenous relative-entropy regularization and optimal investment strategy derived.
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of in…
AI agents manage portfolios, improving on human oversight.
problem Improving strategic asset allocation for institutional investors.
method 50 specialized agents produce capital market assumptions, construct portfolios, critique, and vote on each other's output.
result Meta-agent compares forecasts with realized returns and improves agent performance.
Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a "factor" in the field of finance. Many empirical studies in finance have i…
New framework for portfolio management using binomial markets and game theory.
problem Investment behavior in competitive and incomplete markets.
method Introduces PRFPP framework, constructs and analyzes for both finite and mean field games.
result Relative performance concerns do not always lead to more risky asset investment.
Study optimizes investment strategies in volatile markets using machine learning and Bayesian techniques.
problem Enhancing portfolio management in volatile markets.
method Market segmentation into ten volatility-based states, real-time asset allocation adjustments using Bayesian Markov switching model.
result Dynamic portfolio achieves significantly higher risk-adjusted returns and total returns.
Paper uses AI to predict market trends better than traditional methods.
problem Traditional trend following and momentum investing are limited.
method Uses deep learning and AI techniques for market trend prediction.
result Improves asset manager performance by increasing returns and reducing drawdowns.
The paper models insurance market dynamics under uncertainty and financial frictions.
problem Modeling insurer behavior under uncertainty and financial frictions.
method Dynamic equilibrium model of insurance market with competitive insurers maximizing shareholder value.
result Investment can lead to lower insurance prices and negative loadings under certain conditions.
We will look at the entire cycle of the investment process relating to all aspects of, formulating an investment hypothesis, constructing a portfolio based on that, executing the trades to implement it, on-going risk management, periodically measuring the performance of the portfolio, and rebalancing the portfolio eith…
The study quantifies the impact of fund miscategorization using machine learning.
problem The impact of fund miscategorization on investment decisions.
method Formulated as a distance-based outlier detection problem, used Random Forest based distance metric learning.
result Identified funds with strong relationship to future returns as outliers.
The Heston model optimizes portfolio management based on real market data.
problem Choosing between active and passive investment strategies.
method Calibrated Heston model to real stock market data.
result Passive strategy can outperform active strategy depending on market parameters.
Dynamic rule-based investment strategies outperform static ones in pension schemes.
problem Managing retirement income with dynamic investment strategies.
method Rule-based investment strategies compared to dynamic programming.
result Rule-based strategies achieve higher probability of meeting retirement income targets.
This work models the interconnection of company's investment managers' representations and the market attraction of its shares. The models that reflect the connection of the company's market effectiveness indices and parameters of its economic activity are created on the basis of the Mean-Variance Analysis and Regressi…
Introduces PIT-plot for prioritizing projects based on their impact.
problem Optimizing R&D investments in project portfolios.
method Develops a new tool (PIT-plot) focusing on project impact rather than project properties.
result Identifies projects with the largest impact for risk mitigation or value-adding.
Paper presents a new method for better financial market forecasting.
problem Traditional investment strategies fail to capture market nuances and risks.
method Combines deep learning, factor integration, and correlated stock analysis.
result Enhanced diversification and performance capture in financial markets.
Study shows big winner stocks significantly impact passive and active investment strategies.
problem Impact of big winner stocks on passive and active investment strategies.
method Numerical and analytical techniques applied to historical stock price data.
result Concentrated portfolios underperform equally weighted indexes due to missing big winner stocks.
Selecting the best policy to keep the balance between what a company holds in cash and what is placed in alternative investments is by no means straightforward. We here introduce PyCaMa, a Python module for multiobjective cash management based on linear programming that allows to derive optimal policies for cash manage…
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learningq-fin.PM
Robo-advisor uses ML to optimize investment performance.
problem Maximizing investment performance with historical data.
method Inverse optimization and deep reinforcement learning.
result Robo-advisor consistently outperformed S&P 500.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.
Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocketq-fin.CP
DeepPocket uses graph convolutional reinforcement learning for better financial portfolio management.
problem Maximizing return on investment while managing risk in correlated financial assets.
method Graph convolutional reinforcement learning framework with feature extraction, local information collection, and actor-critic reinforcement learning.
result DeepPocket outperformed market indexes on five real-life datasets over three investment periods, including during the Covid-19 crisis.
The paper analyzes frameworks for integrating sustainability into investment decisions.
problem Understanding how ESG factors influence investment choices.
method Examined and analyzed various theoretical frameworks including Behavioral Finance, Modern Portfolio, and Risk Management.
result Investors increasingly integrate ESG factors to optimize financial outcomes and societal goals.
Combines RL and BF for risk-managed portfolio optimization.
problem Risk management in RL-based portfolio optimization under high volatility.
method Integrates reinforcement learning with barrier functions for dynamic risk control.
result Demonstrates superior performance in real-world data compared to RL-only approaches.